From 6476f5f49b373cd4cf05f2e73389df83e437d597 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 13 Feb 2025 16:30:31 +0100 Subject: Axis legend formatting, update vignettes --- docs/dev/reference/sigma_twocomp.html | 216 ---------------------------------- 1 file changed, 216 deletions(-) delete mode 100644 docs/dev/reference/sigma_twocomp.html (limited to 'docs/dev/reference/sigma_twocomp.html') diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html deleted file mode 100644 index 0ead0184..00000000 --- a/docs/dev/reference/sigma_twocomp.html +++ /dev/null @@ -1,216 +0,0 @@ - -Two-component error model — sigma_twocomp • mkin - - -
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Two-component error model

- Source: R/sigma_twocomp.R - -
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Function describing the standard deviation of the measurement error in -dependence of the measured value \(y\):

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sigma_twocomp(y, sigma_low, rsd_high)
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Arguments

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y
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The magnitude of the observed value

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sigma_low
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The asymptotic minimum of the standard deviation for low -observed values

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rsd_high
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The coefficient describing the increase of the standard -deviation with the magnitude of the observed value

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Value

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The standard deviation of the response variable.

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Details

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$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = -sqrt(sigma_low^2 + y^2 * rsd_high^2)

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This is the error model used for example by Werner et al. (1978). The model -proposed by Rocke and Lorenzato (1995) can be written in this form as well, -but assumes approximate lognormal distribution of errors for high values of -y.

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References

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Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978) -Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry -24(11), 1895-1898.

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Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for -measurement error in analytical chemistry. Technometrics 37(2), 176-184.

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Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical -Degradation Data. Environments 6(12) 124 -doi:10.3390/environments6120124 -.

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Examples

- 
times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times))
-set.seed(123456)
-d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),
-  reps = 2, n = 1)[[1]]
-f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,
- start = list(parent_0 = 100, lrc = -3))
-library(nlme)
-f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
-  data = d_syn, na.action = na.omit,
-  start = list(parent_0 = 100, lrc = -3))
-if (length(findFunction("varConstProp")) > 0) {
-  f_gnls_tc <- update(f_gnls, weights = varConstProp())
-  f_gnls_tc_sf <- update(f_gnls_tc, control = list(sigma = 1))
-}
-f_mkin <- mkinfit("SFO", d_syn, error_model = "const", quiet = TRUE)
-f_mkin_tc <- mkinfit("SFO", d_syn, error_model = "tc", quiet = TRUE)
-plot_res(f_mkin_tc, standardized = TRUE)
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-AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc)
-#>              df      AIC
-#> f_nls         3 114.4817
-#> f_gnls        3 114.4817
-#> f_gnls_tc     5 103.6447
-#> f_gnls_tc_sf  4 101.6447
-#> f_mkin        3 114.4817
-#> f_mkin_tc     4 101.6446
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- - - - - - - - -- cgit v1.2.1